IRL/RL Research

We make an important connection to existing results in econometrics to describe an alternative formulation of inverse reinforcement learning (IRL). In particular, we describe an algorithm using Conditional Choice Probabilities (CCP), which are maximum likelihood estimates of the policy estimated from expert demonstrations, to solve the IRL problem. We show via extensive experimentation on standard IRL benchmarks that CCP-IRL is able to outperform MaxEnt-IRL, with as much as a 5x speedup and without compromising on the quality of the recovered reward function.

Applied Micro Research

Published in Vol. 55, No. 4, November 2014, International Economic Review

This paper studies repeated entry and bidding decisions in construction procurement auctions. We find evidence in the data that suggest the presence of significant cost savings from entering contracts of the same type. We estimate a dynamic auction model to measure the gains to experience for bidders. An auctioneer can increase competition by awarding contracts of the same type in sequence. As a result, procurement costs for each contract can be lowered by 7%, a saving of $110,000.

This paper develops an approach for identifying and estimating the distribution of valuations in ascending auctions where an indeterminate number of bidders have an unknown number of bidding opportunities. To finesse the complications for identification and estimation due to multiple equilibria, our empirical analysis is based on the fact that bidders play undominated strategies in every equilibrium. We apply the model to a monthly financial market in which local banks compete for deposit securities. This market features frequent jump bidding and winning bids well above the highest losing bid, suggesting standard empirical approaches for ascending auctions may not be suitable. We find that frictions are costly both for revenue and allocative efficiency.